Classification of Mouse Chromosomes Using Artificial Neural Networks

这个建议的具体目标是使用新的人工神经网络（ANN）的自适应能力和并行计算性能的小鼠染色体的自动分类。 本课题的总体目标是提供一个计算机软件系统，实现小鼠染色体核型分析过程的自动化，从而大大减少分析时间和人力，提高分析质量和效率。 小鼠染色体的分析在许多遗传学研究领域都具有重要意义。 应用实例包括通过核酸探针与小鼠染色体的原位杂交进行基因定位，物质筛选，其需要在用物质处理后对大量动物（最常见的是小鼠）进行染色体分析，以及测试育种者以维持携带感兴趣的染色体畸变的小鼠的种群。 小鼠染色体比人类染色体更难分类。 虽然自动化的人类核型分析系统是可用的，但没有一个成功地用于自动分类小鼠染色体。 人工神经网络（ANN）代表了一种植根于许多学科的新兴技术。 人工神经网络是一种信息处理系统，具有与生物神经网络相同的某些性能特征。 人工神经网络是作为人类认知或神经生物学的数学模型的推广而开发的。 人工神经网络由网络结构和学习范式组成。 该架构定义了在感兴趣的领域中获得特定知识水平所需的网络的拓扑结构和复杂性，在这种情况下，该知识水平是特定染色体的识别。 学习范式涉及将知识嵌入网络的训练过程。 我们建议设计，训练，测试和评估两种新的人工神经网络，径向基函数（RBF）和概率神经网络（PNN），小鼠染色体的分类。 该上级人工神经网络将被集成到现有的核型分析软件分布，并将取代传统的分类模块。 使用现有的核型分析软件，使我们能够集中在重要的问题上的分类提供的工具，捕捉中期传播下的光学显微镜，分割和增强的数字化图像，以及用户界面。 为实现这一目标，具体计划是： 1.获得小鼠2号染色体的原始图像。 识别小鼠3号染色体的独特特征。开发了特征自动提取程序; 4.准备训练和测试数据集，5.设计、训练和测试径向基函数（RBF）神经网络分类器; 6.设计、训练和测试概率神经网络（PNN）分类器; 7.为小鼠染色体选择最佳的人工神经网络分类器，8.提高分类性能，9.建立小鼠染色体核型分析方法。

The specific goal of this proposal is to use adaptive capabilities and parallel computational properties of novel artificial neural networks (ANNs) for automatic classification of mouse chromosomes. The overall objective is to provide a computer software system to automate the mouse karyotyping process, thus signifcantly reducing the time and human effort neccessary, and improving the quality and efficiency of analysis. Analysis of mouse chormosomes is important to many fields of genetic research. Example applications include gene mapping by in situ hybridization to nucleic acid probes to mouse chromosomes, substance screening which requires chromosome analysis of large numbers of animals (most drequently mice) after treatment with the substance, and testing breeders to maintain stocks of mice that carry chromosome aberrations of interest. Mouse chromosomes are significantly more difficult to classify than human chromosomes. Although automated human karyotyping systems are available, none have been used successfully to classify mouse chromosomes automatically. Artificial Neural Networks (ANNs) represent an emerging technology rooted in many disciplines. An ANN is an information processing system that has certain performance characteristics in common with biological neural networks. ANNs have been developed as generalizations of mathematical models of human cognition or neural biology. An ANN consist of a network architecture and a learning paradigm. The architecture defines the topology and complexity of the network necessary to acquire a specific level of knowledge in the area of interest, which in this case is recognition of specific chromosomes. The learning paradigm involves a training process for embedding the knowledge in the network. We propose to design, train, test, and eveluate two novel ANNs, Radial Basis Function (RBF) and Probabilistic Neural Network (PNN), for classification of mouse chromosomes. The superior ANN will be integrated into an existing karyotyping software distribution and will replace the conventional classification module. Use of the existing karyotyping software allows us to concentrate on the important issue of classification by providing the tools for capture of metaphase spreads under a light microscope , segmentatiion and enhancement of digitized images, as well as a user interface. To achieve the objective of this porposal, the specific plans are to 1. obtain raw images of mouse chromosomes 2. identify distinctive features of mouse chromosomes 3. develop the programs for automatic extraction of the features, 4. prepare the training and testing data sets, 5. design, train, and test the radial basis function (RBF) neural network classifier, 6. design, train, and test the probabilistic neural network (PNN) classifier, 7. select the optimal artificial neural network classifier for mouse chromosomes, 8. improve the classification performance, 9. develop the mouse karyotyping qystem.